import chart_studio.plotly as py
import plotly.figure_factory as ff
import pandas as pd
import numpy as np
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")
table = ff.create_table(df)
iplot(table, filename='jupyter-table1')
init_notebook_mode(connected=True)
data = [go.Bar(x=df.School,
y=df.Gap)]
iplot(data, filename='jupyter-basic_bar')
data[0]
init_notebook_mode(connected=True)
trace_women = go.Bar(x=df.School,
y=df.Women,
name='Women',
marker=dict(color='#ffcdd2'))
trace_men = go.Bar(x=df.School,
y=df.Men,
name='Men',
marker=dict(color='#A2D5F2'))
trace_gap = go.Bar(x=df.School,
y=df.Gap,
name='Gap',
marker=dict(color='#59606D'))
data = [trace_women, trace_men, trace_gap]
layout = go.Layout(title="Average Earnings for Graduates",
xaxis=dict(title='School'),
yaxis=dict(title='Salary (in thousands)'))
fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='jupyter-styled_bar')
init_notebook_mode(connected=True)
s = np.linspace(0, 2 * np.pi, 240)
t = np.linspace(0, np.pi, 240)
tGrid, sGrid = np.meshgrid(s, t)
r = 2 + np.sin(7 * sGrid + 5 * tGrid) # r = 2 + sin(7s+5t)
x = r * np.cos(sGrid) * np.sin(tGrid) # x = r*cos(s)*sin(t)
y = r * np.sin(sGrid) * np.sin(tGrid) # y = r*sin(s)*sin(t)
z = r * np.cos(tGrid) # z = r*cos(t)
surface = go.Surface(x=x, y=y, z=z)
data = [surface]
layout = go.Layout(
title='Parametric Plot',
scene=dict(
xaxis=dict(
gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)'
),
yaxis=dict(
gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)'
),
zaxis=dict(
gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)'
)
)
)
fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='jupyter-parametric_plot')
init_notebook_mode(connected=True)
data = [dict(
visible = False,
line=dict(color='#00CED1', width=6),
name = '𝜈 = '+str(step),
x = np.arange(0,10,0.01),
y = np.sin(step*np.arange(0,10,0.01))) for step in np.arange(0,5,0.1)]
data[10]['visible'] = True
steps = []
for i in range(len(data)):
step = dict(
method = 'restyle',
args = ['visible', [False] * len(data)],
)
step['args'][1][i] = True # Toggle i'th trace to "visible"
steps.append(step)
sliders = [dict(
active = 10,
currentvalue = {"prefix": "Frequency: "},
pad = {"t": 50},
steps = steps
)]
layout = dict(sliders=sliders)
fig = dict(data=data, layout=layout)
iplot(fig, filename='Sine Wave Slider')